{"id":"W4391288959","doi":"10.1016/j.jelectrocard.2024.01.004","title":"CLINet: A novel deep learning network for ECG signal classification","year":2024,"lang":"en","type":"article","venue":"Journal of Electrocardiology","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Greenfield Research (Canada)","funders":"Indian Institute of Technology Roorkee","keywords":"Computer science; Deep learning; Convolution (computer science); Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Code (set theory); Machine learning; Source code; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008207096,0.00009455876,0.0004374995,0.0001680444,0.00006650952,0.00001923351,0.00006176048,0.0001223814,0.0000101086],"category_scores_gemma":[0.000171292,0.0000720426,0.0004472873,0.0002431447,0.00002547481,0.00004441551,0.000006498903,0.0005454443,0.000005993486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008433524,"about_ca_system_score_gemma":0.0001644446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001362224,"about_ca_topic_score_gemma":9.96786e-7,"domain_scores_codex":[0.9990168,0.00005890327,0.0004011406,0.0001376679,0.0001227279,0.0002628264],"domain_scores_gemma":[0.9991648,0.0003100997,0.0001586517,0.00007165714,0.0002141916,0.00008059981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001921089,0.000168543,0.1046473,0.0003006722,0.006858376,0.000253124,0.0002485704,0.02607074,0.4658554,0.002669099,0.009755904,0.3812512],"study_design_scores_gemma":[0.003725357,0.01129372,0.0483955,0.0007057622,0.005198463,0.008506073,0.0003510095,0.5718718,0.00373616,0.003077493,0.3426518,0.0004868419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09479652,0.0129775,0.8890237,0.001809873,0.0007088581,0.0001070995,6.144144e-7,0.00004726354,0.0005285374],"genre_scores_gemma":[0.9820342,0.0005534787,0.01033814,0.0001065854,0.006458061,0.000004936287,0.00000943502,0.0000208604,0.0004743093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8872377,"threshold_uncertainty_score":0.2937812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680964820440812,"score_gpt":0.3193895120562146,"score_spread":0.2925798638518065,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}